Chapter
Building Machine Learning Systems that Understand Language and Causal Relationships
35:17 - 42:55 (07:38)
The challenges in building machine learning systems that can understand language and causal relationships in the world are still significant, but the future of these systems lies in model-based RL and building models that can better generalize to new distributions. Language differences are minute in the grand scheme and the goal is to create systems that can learn from human agents regardless of their language.
Clips
The challenges of machine learning involve building systems that can understand the world and the causal relationships in it and express it in language for reading and writing, regardless of language differences.
35:17 - 37:41 (02:24)
Summary
The challenges of machine learning involve building systems that can understand the world and the causal relationships in it and express it in language for reading and writing, regardless of language differences. The ultimate goal is to replicate the human brain's ability to utilize any language to convey meaning.
ChapterBuilding Machine Learning Systems that Understand Language and Causal Relationships
EpisodeYoshua Bengio: Deep Learning
PodcastLex Fridman Podcast
The history of AI is not just about technical breakthroughs, but also about seminal events that capture the imagination of the community, leading to new markets opening up and allowing for persistence in certain directions.
37:42 - 41:00 (03:18)
Summary
The history of AI is not just about technical breakthroughs, but also about seminal events that capture the imagination of the community, leading to new markets opening up and allowing for persistence in certain directions. There is a big interest from students and professionals alike.
ChapterBuilding Machine Learning Systems that Understand Language and Causal Relationships
EpisodeYoshua Bengio: Deep Learning
PodcastLex Fridman Podcast
Reinforcement learning, or agent learning, has been a successful method in AI for learning policies, but lacks the capability to generalize to new distributions.
41:00 - 42:55 (01:54)
Summary
Reinforcement learning, or agent learning, has been a successful method in AI for learning policies, but lacks the capability to generalize to new distributions. Model-based RL, which uses generative models like GANs, could help agents to understand the world and generalize faster.